Responses of seasonal indicators to extreme droughts in southwest China

Significant impact of extreme droughts on human society and ecosystem has occurred in many places of the world, for example, Southwest China (SWC). Considerable research concentrated on analyzing causes and effects of droughts in SWC, but few studies have examined seasonal indicators, such as variations of surface water and vegetation phenology. With the ongoing satellite missions, more and more earth observation data become available to environmental studies. Exploring the responses of seasonal indicators from satellite data to drought is helpful for the future drought forecast and management. This study analyzed the seasonal responses of surface water and vegetation phenology to drought in SWC using the multi-source data including Seasonal Water Area (SWA), Permanent Water Area (PWA), Start of Season (SOS), End of Season (EOS), Length of Season (LOS), precipitation, temperature, solar radiation, evapotranspiration, the Palmer Drought Severity Index (PDSI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and data from water conservancy construction. The results showed that SWA and LOS effectively revealed the development and recovery of droughts. There were two obvious drought periods from 2000 to 2017. In the first period (from August 2003 to June 2007), SWA decreased by 11.81% and LOS shortened by 5 days. They reduced by 21.04% and 9 days respectively in the second period (from September 2009 to June 2014), which indicated that there are more severe droughts in the second period. The SOS during two drought periods delayed by 3~6 days in spring, while the EOS advanced 1~3 days in autumn. All of PDSI, SWA and LOS could reflect the period of droughts in SWC, but the LOS and PDSI were very sensitive to the meteorological events, such as precipitation and temperature, while the SWA performed a more stable reaction to drought and could be a good indicator for the drought periodicity. This made it possible for using SWA in drought forecast because of the strong correlation between SWA and drought. Our results improved the understanding of seasonal responses to extreme droughts in SWC, which will be helpful to the drought monitoring and mitigation for different seasons in this ecologically fragile region

Investigating plant’s stomatal and non-stomatal responses to water stress via STEMMUS-SCOPE model

Water stress factor is utilized to describe drought effects on plant growth in land surface models (LSMs). Accurately representing water stress is critical to understand the impact of climate change on plant and ecosystem. Models use various approaches to describe the responses of vegetation to water stress. Some models assumed water stress causes stomata closure to attenuate gas exchange process, while others assumed water stress reduces the maximum rate of carboxylation (Vcmax) to slow photosynthesis. Only a few models considered both constraints. However, which parameterization can better capture the dry condition is still controversial. A reliable detection and attribution of the impact of water stress on plant is necessary for understanding the consequence of climate change on the ecosystem from a mechanism aspect. In this study, an empirical stomatal conductance scheme (proposed by Ball et al. in1987, called “BB_gs”) and a unified stomatal conductance model (proposed by Medlyn et al. 2011, called “ME_gs”) were coupled into STEMMUS-SCOPE model to explore the discrepancy between empirical and optimal approaches. Three scenarios were designed to represent the effect of water stress on gas exchange (gs_w), photosynthesis (Vcmax_w) and both processes (gs & Vcmax_w). The coupled model was implemented for three sites with different plant function types, including C3 grassland, C3 shrub, and C4 cropland. Results showed that the optimal stomatal conductance scheme has better performance than the empirical approach because the optimal method considers the realistic stomata regulation. The Vcmax_w scheme captured the drought effects better than other schemes. The results improved our understanding on regional ecosystem functioning under the context of climate change

An Effective Similar-Pixel Reconstruction of the High-Frequency Cloud-Covered Areas of Southwest China

With advantages of multispatial resolutions, a high retrieval accuracy, and a high temporal resolution, the satellite-derived land surface temperature (LST) products are very important LST sources. However, the greatest barrier to their wide application is the invalid values produced by large quantities of cloudy pixels, especially for regions frequently swathed in clouds. In this study, an effective method based on the land energy balance theory and similar pixels (SP) method was developed to reconstruct the LSTs over cloudy pixels for the widely used MODIS LST (MOD11A1). The southwest region of China was selected as the study area, where extreme drought has frequently occurred in recent years in the context of global climate change and which commonly exhibits cloudy and foggy weather. The validation results compared with in situ LSTs showed that the reconstructed LSTs have an average error < 1.00 K (0.57 K at night and −0.14 K during the day) and an RMSE < 3.20 K (1.90 K at night and 3.16 K in the daytime). The experiment testing the SP interpolation indicated that the spatial structure of the LST has a greater effect on the SP performance than the size of the data-missing area, which benefits the LST reconstruction in the area frequently covered by large clouds

Understanding the effects of revegetated shrubs on energy, water and carbon fluxes in a semiarid steppe ecosystem using STEMMUS-SCOPE Model

The revegetation practice is one of the most efficient ways to alleviate soil erosion and desertification. However, the land cover change can considerably disturb ecohydrological processes, particularly in arid and semiarid regions where ecosystems are fragile and suffer intense water stress. This study evaluated the effects of revegetation on the energy, water and carbon fluxes in a desert steppe in Yanchi County, Ningxia Province, Northwest China, by simulating two scenarios of shrubs-grassland and grassland ecosystem with the STEMMUS-SCOPE model. The STEMMUS-SCOPE model integrates canopy photosynthesis, fluorescence, energy balance model and soil water and heat transfer model in the soil-plant-atmosphere continuum system. The model was validated by field observations from May to September of 2016-2019, and showed good performances in simulating the energy, water and carbon fluxes. It indicated that the revegetation facilitated carbon fixation (+69.34%). Latent heat flux was the primary consumer of the available energy and was stronger in the shrubs-grassland ecosystem (+16.76%). With the remarkably increased transpiration of the shrubs-grassland ecosystem (+86.72%), revegetation intensified the soil water losses, especially the soil water content within the 0-200 cm depth (−18.97%). Moreover, the water consumption of the shrubs-grassland ecosystem tended to exceed the received precipitation over the growing seasons. These results emphasized the necessity of considering the adverse impacts of revegetation in future ecological restoration, especially the irreversible soil water depletion and imbalance of energy, water and carbon cycles

STEMMUS-SCOPE for PLUMBER2:: Understanding water-energy-carbon fluxes with a physically consistent dataset across thesSoil-plant-atmosphere (SPAC) continuum

High-quality and long-term measurements of water, energy, and carbon fluxes between the land and atmosphere are critical for eco-hydrological monitoring and land surface model (LSM) benchmarking. Eddy Covariance has become the most widely used method for theory development and LSM evaluation. On the other hand, flux tower data as measured (even after site post-processing and gap-filling based on empirical formulation) cannot be used directly for validating LSMs, and most of time, lacking physically-consistent measurement across the soil-plant-atmosphere continuum (SPAC) (e.g., other than surface fluxes, lacking the measurement of soil moisture, soil water potential, leaf water potential, fluorescence, and reflectance). Here we present high-quality and long-term fluxes and corresponding above/below-ground hydrological, physiological, photosynthetic data derived from the STEMMUS-SCOPE model simulations for PLUMBER2 project with 170 FLUXNET sites. Fluxes data from PLUMBER2 and SM data from FLUXNET2015 are used to further validate the effectiveness of the STEMMUS-SCOPE dataset. Results show that the simulated fluxes and SM dataset have reasonable agreements with the in-situ measurements, using the available global input/forcing datasets without any model tunning. This dataset adds to the existing ecosystem flux and SM network to help increase our understanding of ecosystem responses to extreme events

Towards an open digital twin of soil-plant system following Open Science

Climate projections strongly suggest that the 2022 sweltering summer may be a harbinger of the future European climate. Climate extremes (e.g., droughts and heatwaves) jeopardize terrestrial ecosystem carbon sequestration. The construction of an open digital twin of the soil-plant system helps to monitor and predict the impact of extreme events on ecosystem functioning and could be used to recommend measures and policies to increase the resilience of ecosystems to climate-related challenges. A digital twin refers to a highly interconnected workflow, with a data assimilation framework at its core to combine observations and process-based models, meanwhile accompanied by an interactive and configurable platform that allows users to create and evaluate user-specific scenarios for scientific investigation and decision support. Creating an open digital twin means creating a digital twin following Open Science and FAIR principles, both for data and research software. In this contribution, the STEMMUS-SCOPE model was used as an example to develop an open digital twin of the soil-plant system. We suggest our recently developed open digital twin infrastructure could serve as the backbone for an interoperable framework to facilitate the digitalization of other Earth subsystems (e.g., by simply replacing the soil-plant model). In addition, we show how software not designed initially as open can be adopted to create an open digital twin using containers - standardized computational environments that can be shared, reused and that foster reproducibility

Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison

While a number of machine learning (ML) models have been used to estimate RE, systematic evaluation and comparison of these models are still limited. In this study, we developed three traditional ML models and a deep learning (DL) model, stacked autoencoders (SAE), to estimate RE in northern China's grasslands. The four models were trained with two strategies: training for all of northern China's grasslands and separate training for the alpine and temperate grasslands. Our results showed that all four ML models estimated RE in northern China's grasslands fairly well, while the SAE model performed best (R-2 = 0.858, RMSE = 0.472 gC m(-2) d(-1), MAE = 0.304 gC m(-2) d(-1)). Models trained with the two strategies had almost identical performances. The enhanced vegetation index and soil organic carbon density (SOCD) were the two most important environmental variables for estimating RE in the grasslands of northern China. Air temperature (Ta) was more important than the growing season land surface water index (LSWI) in the alpine grasslands, while the LSWI was more important than Ta in the temperate grasslands. These findings may promote the application of DL models and the inclusion of SOCD for RE estimates with increased accuracy